TitleThe dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells
Data descriptionSingle-cell RNA-seq of 270 human skeletal muscle myoblasts cells
Web-link of the paperhttp://www.nature.com/articles/nbt.2859
Data typescRNA-seq
DatabaseGene Expression Omnibus (GEO)
Accession numberGSE52529
URL of the datahttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE52529
Cell typeSkeletal muscle myoblasts
Number of cells270
Cell capture platformFluidigm C1
Library preparation protocolSMARTer
Unique molecular identifier (Y/N)N
Spike-in (Y/N)N
Full-length (Y/N)N
Brief summary of the scientific questionDescribe Monocle, an unsupervised algorithm that increases the temporal resolution of transcriptome dynamics using single-cell RNA-Seq data collected at multiple time points. Applied to the differentiation of primary human myoblasts, Monocle revealed switch-like changes in expression of key regulatory factors, sequential waves of gene regulation, and expression of regulators that were not known to act in differentiation. Validated some of these predicted regulators in a loss-of function screen. Monocle can in principle be used to recover single-cell gene expression kinetics from a wide array of cellular processes, including differentiation, proliferation and oncogenic transformation.
Brief summary of the bioinformatics processingDescribe an unsupervised algorithm, Monocle, that computationally reconstructs the transcriptional transitions undergone by differentiating cells. Dimensionality reduction. Ordering cells by progress. Identifying branches in a biological process. Differential expression analysis. Clustering genes by pseudotemporal expression pattern. Cell capture and mRNA sequencing. Benchmarking robustness of Monocle. Regulatory sequence analysis. Competitive gene set tests. Transcription factor binding analysis.
CitationTrapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., & Morse, M. et al. (2014). The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nature Biotechnology, 32(4), 381-386.
Web-link of the paperhttp://www.nature.com/articles/nbt.2859